From b032db309949a79b3ecb9ae94a9424c18e965e27 Mon Sep 17 00:00:00 2001
From: Jens Ahrensfeld <jens@jayfield.org>
Date: Sat, 30 May 2026 14:28:57 +0200
Subject: [PATCH] [gaussian_autoencoder] - add GB-RBM autoencoder test with
 Gaussian blob data

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 src/tests/test_gaussian_autoencoder.py | 75 ++++++++++++++++++++++++++
 1 file changed, 75 insertions(+)
 create mode 100644 src/tests/test_gaussian_autoencoder.py

diff --git a/src/tests/test_gaussian_autoencoder.py b/src/tests/test_gaussian_autoencoder.py
new file mode 100644
index 0000000..e042186
--- /dev/null
+++ b/src/tests/test_gaussian_autoencoder.py
@@ -0,0 +1,75 @@
+import random
+import matplotlib.pyplot as plt
+
+from rbm.model import Model
+from rbm.entity import Entity, EntityParams, TrainingParams
+from rbm.image import normalize
+from rbm.matrix import Mat, np
+
+N = 8
+N_VIS = N * N
+N_HID = 32
+N_CASES = 400
+
+
+class TestModel(Model):
+	def __init__(self, name: str, work_dir: str = '.'):
+		super().__init__(name, work_dir)
+		self.unit1 = Entity(
+			(N_VIS, N_HID),
+			EntityParams(do_gaussian_visible=True, do_gaussian_hidden=False),
+			TrainingParams(learning_rate=0.001, momentum=0.9, num_epochs=3000)
+		)
+
+	def forward(self, x: Mat) -> Mat:
+		return self.unit1.forward(x)
+
+	def reconstruct(self, x: Mat) -> Mat:
+		return self.unit1.reconstruct(x)
+
+
+def make_gaussian_blobs(n_cases: int, n: int = N) -> Mat:
+	xs = np.arange(n, dtype=np.float64)
+	ys = np.arange(n, dtype=np.float64)
+	xx, yy = np.meshgrid(xs, ys)
+	data = np.zeros((n_cases, n * n), dtype=np.float64)
+	for i in range(n_cases):
+		cx = random.uniform(1.0, n - 2.0)
+		cy = random.uniform(1.0, n - 2.0)
+		img = np.exp(-((xx - cx) ** 2 + (yy - cy) ** 2) / 4.0)
+		data[i] = img.flatten()
+	return data
+
+
+if __name__ == "__main__":
+	prj_name = "gaussian_autoencoder"
+	work_dir = "results"
+
+	model = TestModel(prj_name, work_dir)
+	model.init(0.01)
+
+	train_batch = normalize(make_gaussian_blobs(N_CASES))
+	model.train(train_batch)
+	model.save()
+
+	test_batch = normalize(make_gaussian_blobs(10))
+	n_show = len(test_batch)
+
+	fig, axes = plt.subplots(2, n_show, figsize=(n_show * 1.5, 3))
+	axes[0, 0].set_ylabel("Original")
+	axes[1, 0].set_ylabel("Recon")
+
+	total_error = 0.0
+	for i, inp in enumerate(test_batch):
+		h = model.forward(inp)
+		recon = model.reconstruct(h)
+		total_error += float(np.mean(np.abs(inp - recon)))
+
+		axes[0, i].imshow(np.asnumpy(np.reshape(inp, (N, N))), cmap='viridis')
+		axes[0, i].axis('off')
+		axes[1, i].imshow(np.asnumpy(np.reshape(recon, (N, N))), cmap='viridis')
+		axes[1, i].axis('off')
+
+	print(f"Mean reconstruction error: {total_error / n_show:.4f}")
+	plt.tight_layout()
+	plt.show()